Confidence Weighted Learning Entropy for Fault-Tolerant Control of a PMSM With a High-Resolution Hall Encoder

There are several nonidealities that can degrade the magnetic Hall-effect sensors performance and impact related applications. Thus, a confidence weighted learning entropy is proposed in this article as a fault-tolerant control strategy for field-oriented control (FOC) of permanent-magnet synchronous machines (PMSMs). It combines sensorless and sensor-based control, while capitalizing on their major advantages, such as operation from standstill and at lower speeds, fast dynamic response, and fault tolerance to encoder errors. Encoder fault detection is based on learning entropy that monitors weights increments of two predictive filters of angular displacement. If the two observed systems behave similarly, the variance of the weights increments is similar. A higher variance, on the other hand, reflects unforeseen misbehavior of a particular system, leading to a decrease in its confidence. A voting mechanism based on confidence weighted average then decides which of the two systems should be used for FOC of the PMSM. The method has been experimentally verified on a high-speed PMSM, achieving more reliable control performance with fast response to encoder failures.